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Boi Il

by io.github.pipeworx-io

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Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

TDQS

A4.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate idempotent and non-destructive. Description adds scope by identifier, persistence for authenticated users, and 24-hour retention for anonymous sessions, which is valuable context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences with clear front-loading: purpose, usage, behavior, and sibling references. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter storage tool with no output schema, the description covers purpose, usage, behavior, persistence details, and sibling integration. Complete and self-contained.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for key and value. Description reinforces key-value pair concept and provides contextual examples like 'subject_property' and 'target_ticker', adding marginal value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool saves data for reuse, with specific verb 'Save' and resource 'data'. It distinguishes from siblings like recall and forget.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use ('when you discover something worth carrying forward') and provides examples. Mentions paired tools recall and forget for retrieval and deletion.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4/5.0
Disambiguation3/5

Most tools have highly detailed descriptions that clarify their distinct roles, and the pipeworx/boi/polymarket families are individually distinguishable. However, ask_pipeworx_beta is explicitly described as currently identical to ask_pipeworx (a true duplicate), and the polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) share overlapping purpose and could cause misselection despite their lengthy docs.

Naming Consistency3/5

Many tools follow a clear verb_noun pattern (compare_entities, discover_tools, resolve_entity, validate_claim), but a large subset uses noun-first or prefixed compound names (ai_visibility_check, bet_research, boi_exchange_rate, polymarket_arbitrage). The naming is readable and group-consistent (boi_*, polymarket_*, ask_pipeworx*) but the overall convention is mixed rather than uniform.

Tool Count2/5

At 34 tools, the set exceeds the 25+ threshold for 'too many' and spans many unrelated domains (data lookup, prediction markets, memory, subscriptions, AI visibility, llms.txt generation, feedback). The broad scope explains the count, but many tools feel like add-on utilities rather than a tightly scoped server, making the surface feel bloated.

Completeness4/5

The core domain—authoritative structured data access—is extremely well covered: universal routing, grounded mode, deep research, entity profiles, comparisons, claim validation, resolution, discovery, and suggestions. Minor gaps exist (no explicit tool for fetching a pipeworx:// citation URI directly, no update operation for subscriptions), but agents can work around these via the router and existing subscription lifecycle tools.

Resources